Seismic inversion by hybrid machine learning
WebAug 15, 2024 · Inverse Problems Solving seismic inverse problems by an unsupervised hybrid machine-learning approach DOI: 10.1190/image2024-3751419.1 Conference: Second International Meeting for Applied... WebSep 15, 2024 · We present a hybrid machine learning (HML) inversion method, which uses the latent space (LS) features of a convolutional autoencoder (CAE) to estimate the …
Seismic inversion by hybrid machine learning
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WebJul 1, 2024 · The main objective of this work is the implementation of Deep Learning (DL) solutions to generate synthetic seismograms from 1D acoustic models without solving the wave equation. This is done by training a NN model which after training is able to predict common shot gathers from 1-D velocity models. The wave equation, is non linear with … WebNov 29, 2024 · To resolve those issues, we employ machine-learning techniques to solve the full-waveform inversion. Specifically, we focus on applying convolutional neural network (CNN) to directly derive the inversion operator f-1 so that the velocity structure can be obtained without knowing the forward operator f.
WebWave-equation-based inversion. Thanks to its unmatched ability to resolve CO 2 plumes, active-source time-lapse seismic is arguably the preferred imaging modality when … WebWe present a new seismic inversion method that uses deep learning (DL) features for the subsurface velocity model estimation. The DL feature is a low-dimensional representation …
WebSeismic inversion is generally carried out by iterative data fitting in which the model updates are evaluated by solving the corresponding physics-based forward modeling. Local optimization methods are commonly used for finding an optimal model. Care must be taken to account for the ill posedness of the problem by imposing proper constraints. WebNov 1, 2024 · This leads to simultaneous inversion of P- and S-wave velocity and as well as density as shown in Fig. 6. Download : Download high-res image (464KB) Download : Download full-size image; Fig. 6. Architecture of the PINN for solving 1D seismic wave equation involving with linear elastic equations for inversion of P- and S-wave velocities …
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WebTo mitigate the cycle-skipping problem, Bunks et al. (1995) propose a multiscale inversion approach that initially inverts low-pass-filtered seismic data and then gradually admits higher frequencies as the iterations proceed. AlTheyab and Schuster (2015) remove the mid- and far-offset cycle-skipped seismic traces before inversion and gradually incorporate … scrapy retry_http_codesWebFeb 17, 2024 · Seismic inversion is generally carried out by iterative data fitting in which the model updates are evaluated by solving the corresponding physics-based forward modeling. Local optimization methods are commonly used for finding an optimal model. Care must be taken to account for the ill posedness of the problem by imposing proper constraints. scrapy robotstxtWebJan 24, 2024 · Seismic inversion is a process to obtain the spatial structure and physical properties of underground rock formations using surface acquired seismic data, constrained by known geological laws and drilling and logging data. The principle of seismic inversion based on deep learning is to learn the mapping between seismic data and rock properties … scrapy rexWebThrough synthetic tests and the application of real data, we show the reliability of the physics informed machine learning based traveltime inversion which can be a potential alternative tool to the traditional tomography frameworks. Keywords: inverse problems, machine learning, seismic traveltimes, physics informed neural networks scrapy robot.txtWebDeep learning-based methods gain great popularity because of their powerful ability to obtain exact solutions for geophysical inverse problems. However, those deep learning methods that use seismic data as the only input lead to difficult training and unstable inversion results (i.e., transverse discontinuity or geologic unreliability). scrapy rule followWebJan 15, 2024 · microsoft computer-vision deep-learning neural-networks segmentation seismic seismic-inversion seismic-imaging seismic-data seismic-processing Updated on Sep 18, 2024 Python gem / oq-engine Star 301 Code Issues Pull requests OpenQuake's Engine for Seismic Hazard and Risk Analysis scrapy return itemWebJan 5, 2024 · The S-wave velocity is a critical petrophysical parameter in reservoir description, prestack seismic inversion, and geomechanical analysis. However, obtaining … scrapy run from python